Systems for Generation of Liability Protection Policies

ABSTRACT

Systems and methods for intellectual property (IP) asset protection are disclosed. For example, by analyzing characteristic information associated with a user, along with feedback from one or more potential insurers, the system may determine various terms of an insurance policy for protecting the user against claims of IP infringement. The policy may provide for financial reimbursement for costs incurred while taking active measures to mitigate losses and/or defend against such claims. In addition, the system may analyze the characteristic information to identify various users having a low exposure of an infringement claim being asserted. The identified users may be actively targeted to acquire an IP protection policy.

RELATED APPLICATION

This application claims priority to and is a continuation of U.S. patentapplication Ser. No. 16/542,799, filed on Aug. 16, 2019, which willissue as U.S. Pat. No. 11,514,529 on Nov. 29, 2022, which claims thebenefit of and priority to U.S. Provisional Patent Application Ser. No.62/855,550, filed on May 31, 2019 and titled “Systems for Generation ofLiability Protection Policies”, all of which are herein incorporated byreference in their entirety.

BACKGROUND

Businesses often acquire insurance for a variety of protection purposes,such as claims of personal injury or employment-related issues. Oneexposure many businesses face is the burdensome costs that may accrue inthe defense against, and resolution of, allegations of intellectualproperty (IP) infringement. However, unlike some other risks and formsof insurance protection, it may be difficult to assess the business'sexposure to IP infringement allegations and/or the policy termsassociated with an appropriate IP liability protection policy. Describedherein are improvements in technology and solutions to technicalproblems that can be used to, among other things, formulate the termsand conditions of insurance policies geared towards intellectualproperty liabilities.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth below with reference to theaccompanying figures. In the figures, the left-most digit(s) of areference number identifies the figure in which the reference numberfirst appears. The use of the same reference numbers in differentfigures indicates similar or identical items. The systems depicted inthe accompanying figures are not to scale and components within thefigures may be depicted not to scale with each other.

FIG. 1 illustrates a schematic diagram of an example environment forintellectual property (IP) liability protection.

FIG. 2 illustrates example phases for initiating IP liabilityprotection.

FIG. 3 illustrates an example server computing device that may be usedfor IP liability protection.

FIG. 4 illustrates a flow diagram of an example process for IP liabilityprotection.

FIG. 5 illustrates a flow diagram of another example process for IPliability protection.

FIG. 6 illustrates a flow diagram of another example process for IPliability protection.

DETAILED DESCRIPTION

Systems and methods for intellectual property (IP) risk protection aredisclosed herein. In particular, the systems and methods describedherein provide techniques for determining and/or generating the termsfor an IP liability protection policy to compensate the policy owner forcosts incurred in the defense and resolution of a claim for IPinfringement. For example, a company offering one or more productsand/or services may wish to obtain an insurance policy to protectagainst claims that the products or services infringe the intellectualproperty rights of some third party or parties. The system allows foruser information associated with a user trying to obtain an IP liabilitypolicy (e.g., a business associated with the user) to be gathered andanalyzed in order to determine various characteristic information. Thecharacteristic information may include such information as a businessclassification, employee information, revenue information, litigationhistory, entity status, IP asset information, and the like, for example.This information may be utilized to determine one or more infringementexposure values associated with the user representing an exposure thatthe user will have a claim for IP infringement asserted against them. Inaddition, the system may solicit feedback from a potential insurer. Theinsurer feedback may be utilized to adjust the analysis that has beenperformed and/or to determine a cost associated with insuring the user.It should be understood that anytime the word “user” is used herein,that term includes individuals and/or entities and/or, in the context ofsending information using computing devices, computing devices.

Unlike conventional protection policies acquired by users, such aspolicies covering claims related to injuries, employment issues, and thelike, it may be difficult to evaluate the potential exposure associatedwith an IP liability protection policy. As such, conventional policiesrelating to IP liability often involve a costly and time-consumingevaluation process. In addition, conventional policy terms may not bebased on data associated with the user, such as the characteristic dataand infringement exposure values utilized by the system herein. As such,the pricing and/or other policy details associated with conventional IPliability policies may be difficult to obtain, poorly reflect the risksof a specific user, and lack stability.

The techniques for determining IP liability policy terms as describedherein, however, help to formulate more accurate and efficient policyterms for providing protection related to IP risks. Additionally, the IPliability policy terms determined and/or generated may expand coverage.For example, terms may include coverage for loss mitigation techniques,such as design-around services aimed at mitigating future losses.Further, the terms may include coverage for contractual indemnities owedunder existing contractual obligations (e.g., contractual obligations toexisting customers). Still further, as a result of the improvedtechniques described herein, co-insurance terms may be decreased and theaggregate limit of the IP liability policy may be increased, thusresulting in extended coverage, along with both a lower financialobligation for the user based on a better understanding of potentialpayouts under the policy.

In some examples described below, the system may receive a request forinsurance policy coverage from a user for claims relating to IPliability. The user may be, or may represent, a business producing oneor more goods for sale and/or offering one or more services. Inaddition, the business may have one or more IP assets, such as patents,trademarks, and the like, for example. The user may wish to obtain apolicy providing protection against costs and liability (e.g., financialliability and/or legal liability) resulting from legal claims and, inparticular, IP infringement claims. For example, the user may seek apolicy that provides reimbursement for defense of an infringement claimbrought by a third-party. Defense of the claim may include legal fees,mitigation costs (e.g., design-around costs), settlements and damages,indemnification costs, and the like.

The user may submit a request, along with preliminary information, tothe system that may be used to determine one or more terms of the policyand/or generate policy recommendations. The preliminary information mayinclude information relating to one or more characteristics of the usersuch as business information, IP asset information, and/or a legalhistory. Alternatively, or in addition, upon receiving the request fromthe user, the system may request information from the user. Stillfurther, in response to receiving the request, the system may access oneor more local and/or third-party databases having information relatingto the user such as characteristics of the user. For instance, thesystem may determine that the user has not provided certain informationrelating to the characteristics and may access one or more databases togather the necessary information regarding the user.

In examples, in response to receiving the request, the system mayprovide a graphical user interface (GIU) to a device of the user and/orthe user having one or more fields for receiving the request and/orrequesting information associated with the characteristics. Forinstance, the system may be configured to receive the request along withpreliminary information indicating a type of user (e.g., a businesscategory of the user, a type of policy the user is seeking, etc.). Basedon the preliminary information, the system may generate various GUIelements configured to receive and/or request input of informationaccording to the type of user indicated (e.g., the information receivedand/or requested may be customized according to the type of user). TheGUI may receive the input from the user and generate input data for useby the system. The GUI may generate input data that is encrypted orotherwise protected for transfer to the system. Further, the input datamay indicate various indicators of the input data, such as a time theinput data was received, an identifier of the user, and the like, forexample.

In further examples, the system may analyze the user-providedinformation and/or the accessed information to generate characteristicdata associated with the one or more characteristics of the user. Forexample, the user may provide business information, such asclassification information, which the system may analyze to determinethe standard industrial classification (SIC) code associated with theuser's business. Additionally, the system may analyze thereceived/accessed information to determine and/or generatecharacteristic data associated with the user's business including, butnot limited to, an employee count, revenue size, litigation history,whether the company is publicly or privately held, IP assets, and thelike, for example.

As described herein, the characteristic data may be utilized todetermine an infringement exposure value associated with the user. Forexample, the characteristic data may be used by the system to determinean infringement exposure value indicating a predicted exposure that aninfringement claim may be brought against the user. In particular, theinfringement exposure value may indicate a predicted frequency and/orseverity of potential infringement claims against the user (e.g., anumber of claims that may be brought against the user annually and/or apredicted value of monetary liability), and/or an exposure (e.g., howexposed the user is to infringement claims based on the currentpolicies, such as existing policies already in place to protect variousproducts/services against claims of infringement, and/or IP assets inplace). In some examples, one or more infringement exposure values maybe determined. For example, the system may determine an infringementexposure value on the basis of each revenue source, such as a product,service, or any other revenue-producing offering (e.g., in associationwith each product/service owned, manufactured, and/or provided by theuser). The infringement exposure values may vary according to thevarious characteristic data, with each characteristic analyzed on itsown or in combination with other characteristic data. For example, theinfringement exposure value for a given revenue source may be higher fora revenue source having a litigation history (e.g., a previousinfringement claim) and/or a limited IP footprint (e.g., if the userdoes not have any issued patents, trademarks, etc. for the given revenuesource).

In some examples, the system may provide the one or more infringementexposure values to an insurer for review. For example, the system mayprovide the infringement exposure value(s) to one or more potentialinsurers that will be providing/carrying the policy. The insurer(s) willhave the ability to review the infringement exposure value(s), alongwith the characteristic data and/or any other data accessible to theinsurer(s). The insurer may provide feedback regarding the infringementexposure value(s) to the system. For example, the insurer may providefeedback that the infringement exposure value(s) are too high/low forthe user and/or the given revenue source. Additionally, oralternatively, the feedback may indicate that the insurer requests moreinformation to evaluate and/or accept the proposed infringement riskexposure value(s). The system may use the feedback to adjustinfringement exposure value(s) and future analysis accordingly.

Additionally, or alternatively, the systems described herein may beconfigured to provide a rating and/or other metric for a particularpotential-insured and/or may rank or otherwise prioritizepotential-insureds. The rating may be based at least in part on theinformation described herein. The ranking may be based at least in parton the ratings of other potential insureds and/or on an exposure profileassociated with a given insurer and/or insurer category. For example, afirst exposure profile may be associated with a first insurer, and thefirst exposure profile may indicate a certain amount degree of exposurethat the first insurer is willing to tolerate. A second exposure profileassociated with a second insurer may indicate a lower or higher degreeof exposure than the first insurer. These factors may be considered whenranking potential insureds. Additionally, in examples, a given exposureprofile may include an exposure portfolio. The exposure portfolio mayindicate a number of desired insureds that the insurer desires toinsurer that have certain characteristics. For example, the insurer maydesire to insure a relatively low number of high-exposure entities and ahigher number of low-exposure entities, which may represent a diverseexposure portfolio. In these examples, depending on a current exposureportfolio and how policies that have been issued correlate to theexposure portfolio, a given entity and/or potential insurance policy maybe ranked higher than others.

The present disclosure provides an overall understanding of theprinciples of the structure, function, manufacture, and use of thesystems and methods disclosed herein. One or more examples of thepresent disclosure are illustrated in the accompanying drawings. Thoseof ordinary skill in the art will understand that the systems andmethods specifically described herein and illustrated in theaccompanying drawings are non-limiting embodiments. The featuresillustrated or described in connection with one embodiment may becombined with the features of other embodiments, including as betweensystems and methods. Such modifications and variations are intended tobe included within the scope of the appended claims.

Additional details pertaining to the above-mentioned techniques aredescribed below with reference to several example embodiments of FIGS.1-6 . It is to be appreciated that while these figures describe exampleenvironments and devices that may utilize the claimed techniques, thetechniques may apply equally to other environments, devices, and thelike.

FIG. 1 illustrates a schematic diagram of an example environment 100 forintellectual property (IP) liability protection. In some examples, theenvironment 100 may include one or more user devices 102 associated witha user 104, also described herein as electronic devices 102 and/orclient-side devices 102, and a policy generation system 106 that isremote from, but in communication with, the client-side electronicdevices 102 via a network 108. The environment 100 may also include aninsurer system 110 associated with an insurer 112 that is remote from,but in communication with, the user devices 102 and/or the policygeneration system 104 via the network 108. The environment 100 mayfurther include one or more third-party databases 114 that are remotefrom, but in communication with, the policy generation system 104 and/orthe insurer system 110.

The user devices 102 may include components such as, for example, one ormore processors 116, one or more network interfaces 118, memory 120, oneor more displays 122, and/or one or more input elements 124. The memory118 may include components such as, for example, one or moreapplications 124. As shown in FIG. 1 , the user devices 102 may include,for example, a computing device, a mobile phone, a tablet, a laptop,and/or one or more servers. It should be understood that the examplesprovided herein are illustrative and should not be considered theexclusive examples of the components of the user device 102.Additionally, one or more of the components of the user device 102 maybe generally utilized to perform one or more of the actions, operations,and/or steps described herein as being performed by the user 104.

The policy generation system 106 may include components such as one ormore processors 128, one or more network interfaces 130, and/or memory132. The memory 132 may include one or more components such as one ormore policy-term generation components 134. It should be understood thatthe examples provided herein are illustrative and should not beconsidered the exclusive examples of the components of the policygeneration system 106. Additionally, one or more of the components ofthe policy generation system 106 may be generally utilized to performone or more of the actions, operations, and/or steps described herein.

For example, as described herein, the user 104 may submit a request tothe policy generation system 106 for an insurance policy 140 associatedwith IP liability. For instance, the user 104 may submit a request foran insurance policy quote to the policy generation system 106. Alongwith the request, the user 104 may provide various information, such asinformation related to a business of the user 104. More specifically,the user may provide information relating to various characteristics ofthe user's 104 business, referred to herein as characteristicinformation 136. The characteristic information 136 may includeinformation relating to various characteristics of the business such asclassification information (e.g., one or more standard industrialclassification (SIC) codes), an employee count, revenue information,litigation history, whether the company is publicly traded or privatelyheld, and/or information regarding any IP assets. This information maybe utilized by the system to determine one or more terms of an insurancepolicy 140 and/or generate an insurance policy 140 associated with apotential IP liability of the user.

Alternatively, or in addition, the policy generation system 106 mayrequest information from the user 104 in response to receiving therequest. For example, in response to receiving the request, the policygeneration system 106 may provide an information request to the user104. The information request may include an intake form requesting thecharacteristic information 136, as described herein. Additionally, ifthe policy generation system 106 determines that the initialcharacteristic information 136 provided by the user 104 along with therequest does not contain all of the required information necessary todetermine the policy terms, the policy generation system 106 may requestadditional characteristic information 136 from the user 104. In thisway, the policy generation system 106 may ensure that all of therequired information is gathered from the user 104.

Still further, in some examples, the policy generation system 106 maycommunicate with one or more third-party databases 114 that are remotefrom, but in communication with, the policy generation system 104 togather information about the user 104. For example, upon receiving therequest for the policy, the policy generation system 106 maycommunicate, via the network 108, with one or more third party databases114 to obtain the required information associated with thecharacteristics of the user 104. The third-party database(s) 114 mayinclude information regarding the user 104, such as informationhistorically provided by the user 104, publicly available informationregarding the user 104, and the like, for example. Alternatively, or inaddition, the policy generation system 106 may access the third-partydatabases 114 in response to receiving the initial characteristicinformation 136 and/or the requested characteristic information 136(e.g., from the information request or intake document). For example,should the provided characteristic information 136 not include all ofthe necessary information, the policy generation system 106 may accessand/or receive information from the third-party databases 114 to obtainthe missing information.

One the policy generation system 106 has received and/or accessed therequired characteristic information 136 from the user 104 and/or thethird-party database(s) 114, the system may utilize the characteristicinformation 136 to determine key characteristic data associated with theuser 104. As described herein, the key characteristic data may include,but not be limited to, one or more SIC codes associated with the user104. The SIC code(s) may include one or more four-digit codes used toclassify an industry practiced by the business of the user 104. The keycharacteristic data may further include employment informationassociated with the user 104. For example, the employment informationmay include a number of employees of the user 104, the role of eachemployee, and the like. In addition, the key characteristic data mayinclude a revenue size of the user 104. For example, the revenue datamay include revenue of the user 104 for a predetermined period of time,such as annually, quarterly, and the like. Further, the keycharacteristic data may include a litigation history associated with theuser 104. The litigation history may include data associated with afrequency, cost incurred, annualized expenditure for litigation costs,and information regarding each claim (e.g., bringing or defending aclaim, ruling, settlement terms, etc.). Still further, the keycharacteristic data may include data indicating whether the user 104 hasa company that is privately held or publicly traded.

Each of the key characteristic data components may be broken down bybusiness unit, product service (e.g., any revenue-generating element),and/or SIC code of the user 104. For example, the business of the user104 may have one or more units having one or more associated SIC codes,such as a subsidiary, multiple units with different product offerings,and the like. As such, the key characteristic data may be determinedaccording to each business unit.

In addition, the key characteristic data may further include IP dataassociated with the user 104. For example, the characteristicinformation 136 may including information regarding one or more IPassets of the user 104 and indicating an IP footprint associated withthe user 104. For instance, the user 104 may provide and/or the policygeneration system 106 may access from the third-party database(s) 114,information regarding each IP asset of the user 104. Using thisinformation, the policy generation system 106 may determine IP dataassociated with the user 104.

The information associated with the IP asset(s) may include adescription of the IP associated with the user 104, types andidentifiers of the IP and/or registration of the IP, the countries inwhich the IP is protected, a description of the IP strategy or functionassociated with each IP asset, origination, age, identification of theindividuals and/or firms responsible for management and/or oversight ofthe IP portfolio, estimated revenue and profit attributable to the IP,identification and description of IP licensing to third parties,historical exercise of IP rights associated with products and/orservices offered by the user, description of how revenues and/or profitsfrom IP are distributed by each business unit and/or by country,previous valuations of the IP, copies of licenses and/or agreementsrelated to the IP, identification of any known, unlicensed third-partyuse of the IP, identification of any IP that has been threatened,challenged, and/or subject to administrative and/or judicial actions,copies of standard employment contracts and identification of assignmentprovisions in those employment contracts, identification of keyemployees associated with the IP and whether those employees have leftthe user 104, the licensing of third-party intellectual property by theuser 104, procedures to avoid infringement of third-party IP rights,whether the user 104 already has any insurance policies in place fortheft of IP and/or liability for infringement of third-party IP,identification of potential purchasers of the IP, and/or identificationof key competitors.

Utilizing the key characteristic data, the policy generation system 106may analyze the key characteristic data to determine an infringementexposure value 138 associated with the user 104. The infringementexposure value 138 may indicate an exposure value that a claim for IPinfringement will be brought against the user 104. For instance, theinfringement exposure value 138 may represent an exposure valueassociated with a predicted frequency and/or severity of an IPinfringement claim, and/or exposure of the user 104 to a given IPinfringement claim. In particular, the infringement exposure value 138may represent a predicted value of financial liability associated withthe user 104 (e.g., costs, damages, settlements, etc. associated withthe policy). Alternatively, or in addition, the infringement exposurevalue(s) 138 may be determined on a per product, service, and/or revenuesource basis. For example, the policy generation system 106 may predicta liability for each revenue source of the user 104 (e.g., an exposurevalue that a claim for IP infringement will be brought in associationwith the given revenue source).

In addition, in examples described herein, the policy generation system106 may determine the infringement exposure value(s) 138 in light of theIP data. For example, in light of the IP data, the infringement exposurevalue(s) 138 may be increased and/or decreased. For instance, if the IPdata indicates that the user 104 has an issued patent in associationwith a revenue source, the infringement exposure value 138 associatedwith the user 104 and/or that revenue source may indicate a low exposurevalue because a third-party IP holder that makes a competing product maybe risk an infringement counterclaim by the user under its issuedpatent. Alternatively, if the IP data indicates that the user 104 doesnot have any issued patents, or IP protection, for a given revenuesource, the infringement exposure value 138 may indicate a high exposurevalue. In another example, if the IP data indicates that the user 104already has an insurance policy in association with a given IP asset,the infringement exposure value 138 may likewise indicate a low exposurevalue (e.g., the policy issued under the system described herein wouldonly apply to claims and/or coverage in excess of the policies alreadyin place and, thus, would reduce the amount paid out by potentialinsurers).

It should also be understood that anywhere in this disclosure where theterm “trade secret” is used, it should be noted to include not onlytrade secrets, but any document and/or data and/or information includingconfidential information, know-how, and other information, and notnecessarily documents, data, and/or information meeting a legaldefinition of the term “trade secret.”

In examples, the infringement exposure value(s) 138 may be provided toan insurer 112 for review. For example, the insurer 112, or insurancecarrier, may include one or more potential individuals or companies thatwill carry the IP liability policy, or insurance policy 140, of the user104. The infringement exposure value(s) 138 may be provided to theinsurer system 110 via the network 114. The insurer 112 may then analyzethe infringement exposure value(s) 138 to determine if the value(s)should be adjusted. For example, the insurer 112 may utilize additionalinformation associated with the user 104, historical data, industrydata, and the like, to determine if the infringement exposure value(s)138 reflect an accurate or acceptable prediction of IP liabilityassociated with the user 104. The insurer feedback data 142 may includeother information and/or insurer 112 comments regarding the infringementexposure value(s) 138, such as any risk premium or discount assigned tothe values based upon the overall portfolio of policies issued by theinsurer or its exposure to risks associated with particular industriesor technologies. The insurer 112 may provide, via the insurer system110, insurer feedback data 142 to the policy generation system 106.

In examples, the policy-term generation component(s) 134 of the policygeneration system 106 may utilize the infringement exposure value(s) 138and/or the insurer feedback data 142 to determine an indication of termsfor insuring the user 104. The policy generation system 106 mayrecommend terms for insuring the user 104 and/or generate an insurancepolicy 140 including all the terms for insuring the user 104. Forexample, the policy-term generation component(s) 134 may be stored inassociation with a managing general agent, with the managing generalagent having the ability to generate and/or bind the insurer.

The insurance policy 140 may include, but not be limited to, termsassociated with a co-insurance value (e.g., a deductible the user 104 isresponsible for), defense and/or resolution coverage, contractualindemnity responsibilities, loss mitigation procedures, and the like,for example. The insurance policy 140 may be provided to the user device102 for review and acceptance by the user 104. In addition, theinsurance policy 140 may be provided to the insurer 112 for review andacceptance. In response to both parties, the user 104 and the insurer112, accepting the terms of the insurance policy 140, the policy will befinalized by the insurer 112 and become effective.

As shown in FIG. 1 , several of the components of the policy generationsystem 106 and the associated functionality of those components asdescribed herein may be performed by one or more of the other systemsand/or by the user device 102. Additionally, or alternatively, some orall of the components and/or functionalities associated with the userdevice 102 may be performed, at least in part, by the policy generationsystem 108.

It should be noted that the exchange of data and/or information asdescribed herein may be performed only in situations where a user hasprovided consent for the exchange of such information. For example, auser may be provided with the opportunity to opt in and/or opt out ofdata exchanges between devices and/or with the systems and/or forperformance of the functionalities described herein. Additionally, whenone of the devices is associated with a first user account and anotherof the devices is associated with a second user account, user consentmay be obtained before performing some, any, or all of the operationsand/or processes described herein.

As used herein, a processor, such as processor(s) 116 and/or 128, mayinclude multiple processors and/or a processor having multiple cores.Further, the processors may comprise one or more cores of differenttypes. For example, the processors may include application processorunits, graphic processing units, and so forth. In one implementation,the processor may comprise a microcontroller and/or a microprocessor.The processor(s) 116 and/or 128 may include a graphics processing unit(GPU), a microprocessor, a digital signal processor or other processingunits or components known in the art. Alternatively, or in addition, thefunctionally described herein can be performed, at least in part, by oneor more hardware logic components. For example, and without limitation,illustrative types of hardware logic components that can be used includefield-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), complex programmable logic devices(CPLDs), etc. Additionally, each of the processor(s) 116 and/or 128 maypossess its own local memory, which also may store program components,program data, and/or one or more operating systems.

The memory 124 and/or 132 may include volatile and nonvolatile memory,removable and non-removable media implemented in any method ortechnology for storage of information, such as computer-readableinstructions, data structures, program component, or other data. Suchmemory 124 and/or 132 includes, but is not limited to, RAM, ROM, EEPROM,flash memory or other memory technology, CD-ROM, digital versatile disks(DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, RAID storagesystems, or any other medium which can be used to store the desiredinformation and which can be accessed by a computing device. The memory124 and/or 132 may be implemented as computer-readable storage media(“CRSM”), which may be any available physical media accessible by theprocessor(s) 116 and/or 128 to execute instructions stored on the memory124 and/or 136. In one basic implementation, CRSM may include randomaccess memory (“RAM”) and Flash memory. In other implementations, CRSMmay include, but is not limited to, read-only memory (“ROM”),electrically erasable programmable read-only memory (“EEPROM”), or anyother tangible medium which can be used to store the desired informationand which can be accessed by the processor(s).

Further, functional components may be stored in the respective memories,or the same functionality may alternatively be implemented in hardware,firmware, application specific integrated circuits, field programmablegate arrays, or as a system on a chip (SoC). In addition, while notillustrated, each respective memory, such as memory 124 and/or 136,discussed herein may include at least one operating system (OS)component that is configured to manage hardware resource devices such asthe network interface(s), the I/O devices of the respective apparatuses,and so forth, and provide various services to applications or componentsexecuting on the processors. Such OS component may implement a variantof the FreeBSD operating system as promulgated by the FreeBSD Project;other UNIX or UNIX-like variants; a variation of the Linux operatingsystem as promulgated by Linus Torvalds; the FireOS operating systemfrom Amazon.com Inc. of Seattle, Wash., USA; the Windows operatingsystem from Microsoft Corporation of Redmond, Wash., USA; LynxOS aspromulgated by Lynx Software Technologies, Inc. of San Jose, Calif.;Operating System Embedded (Enea OSE) as promulgated by ENEA AB ofSweden; and so forth.

The network interface(s) 118 and/or 130 may enable messages between thecomponents and/or devices shown in architecture 100 and/or with one ormore other remote systems, as well as other networked devices. Suchnetwork interface(s) 118 and/or 130 may include one or more networkinterface controllers (NICs) or other types of transceiver devices tosend and receive messages over a network 108.

For instance, each of the network interface(s) 118 and/or 130 mayinclude a personal area network (PAN) component to enable messages overone or more short-range wireless message channels. For instance, the PANcomponent may enable messages compliant with at least one of thefollowing standards IEEE 802.15.4 (ZigBee), IEEE 802.15.1 (Bluetooth),IEEE 802.11 (WiFi), or any other PAN message protocol. Furthermore, eachof the network interface(s) 118 and/or 130 may include a wide areanetwork (WAN) component to enable message over a wide area network.

FIG. 2 illustrates example phases for initiating IP liability protectionutilizing a policy generation system 202, such as system 106 of FIG. 1 .The example phases may include a first phase 204, a second phase 206,and a third phase 208. The phases described herein illustrate the phasesof a user requesting and receiving an insurance policy pertaining to IPliability, as described herein.

For example, in the first phase 204, a user 210 may submit a request forinsurance to the policy generation system 202 via a network 212. Theuser 210 may submit the request through an application or web-basedinterface of a user device, such as user device(s) 102 described in FIG.1 . The request may include information about the user 210 and/or abusiness associated with the user 210. The policy generation system 202may analyze the information to generate characteristic data associatedwith one or more key characteristics of the user 210.

Alternatively, or in addition, in response to receiving the request fromthe user 210, the policy generation system 202 may provide aninformation request to the user 210 requesting information associatedwith the one or more key characteristics. For example, the user 210 maybe provided with an intake document requesting various informationassociated with one or more key characteristics of the user 210. Theintake document may vary in length and depth of information requested.

Using this information, the policy generation system 202 may analyze theprovided information to generate characteristic data associated with thekey characteristics of the user. The characteristic data may include oneor more classification codes associated with the business of the user210 (e.g., one or more standard industrial classification (SIC) codes),an employee count, revenue information, litigation history, whether thecompany is publicly traded or privately held, and/or informationregarding any IP assets.

In some examples, the policy generation system 202 may analyze theinformation provided by the user 210 and determine that informationassociated with the key characteristics is missing. In this example, thesystem may request additional information from the user 210.Alternatively, or in addition, the system may access one or more localand/or third-party databases containing information about the user 210.For example, the user 210 may have previously provided information tothe policy generation system 202 and/or the policy generation system 202may have gathered information associated with the user 210 (e.g.,historical information such as preferences, transactions, etc.). Theinformation may have been stored in a component of the policy generationsystem 202. Alternatively, or in addition, the policy generation system202 may communicate with one or more third-party databases havinginformation associated with the user 210. For example, the policygeneration system 202 may access a third-party database and/or submit arequest for information associated with the user 210.

In the second phase 206, an infringement exposure value(s) component ofthe policy generation system 202 may determine one or more exposurevalue(s) associated with the user 210. As described herein, theinfringement exposure value(s) may represent a predicted exposure valuethat an IP infringement claim may be brought against the user 210. Inparticular, the infringement exposure value(s) may indicate a predictedfrequency and/or severity of potential infringement claims against theuser 210 (e.g., a number of claims that may be brought against the user210 annually and a predicted value of monetary liability), and/or anexposure (e.g., how exposed the user 210 is to infringement claims,based upon what IP protection the user 210 has in place or othermitigation measures that may have been taken such as throughfreedom-to-operate analyses, design-around practices, and/or licensingof third-party IP).

For example, the infringement exposure value(s) component may analyzethe characteristic data associated with the key characteristics of theuser 210 to determine the infringement exposure value(s). For instance,the key characteristic data may indicate that the user 210 has a historyof infringement litigation. In this example, the infringement exposurevalue component(s) may determine a high infringement exposure value forthe user 210. In another example, the key characteristic data mayindicate that the user 210 is a large corporation that produces many andvaried products, thus potentially creating a higher exposure that theuser 210 will have an infringement claim brought against one of them.Still further, the key characteristic data may include IP assetinformation (e.g., the IP footprint of the user 210). For instance, inthe previous example, while the infringement exposure value(s) componentmay have ordinarily determined a higher infringement exposure value forthe user 210 due to the large number and/or varied scope of productofferings, the IP asset information may indicate that the user 210 haveIP assets (e.g., one or more patents and/or other types of IPprotection) that reduce risks associated with the product offerings. Inthis example, in light of the IP assets associated with the productofferings, the infringement exposure value(s) component may determine alower infringement exposure value associated with the user 210, thusindicating a lower exposure value of an IP infringement claim beingbrought against the user 210.

In examples, the infringement exposure value component(s) may utilizeone or more machine learning techniques to determine the infringementexposure value(s) associated with the user 210. For example, theinfringement exposure value component(s) may execute one or morealgorithms (e.g., decision trees, artificial neural networks,association rule learning, or any other machine learning algorithm) totrain the system to determine the one or more infringement exposurevalues based on historical user data, transactional data, policyperformance, and the like. In examples, the machine learningcomponent(s) may execute any type of supervised learning algorithms(e.g., nearest neighbor, Naïve Bayes, Neural Networks, unsupervisedlearning algorithms, semi-supervised learning algorithms, reinforcementlearning algorithms, and so forth).

Once the infringement exposure value(s) have been determined, theinfringement exposure value(s) may be provided to an insurer 214 forevaluation. The insurer 214 may represent an insurance carrier, or,specifically, one or more potential individuals or companies that willcarry the insurance policy. The infringement exposure value(s) 138 maybe provided to the insurer system 110 via the network 212. The insurer214 may receive the infringement exposure value(s) and perform ananalysis to determine if the value(s) accurately or acceptably reflectthe predicted IP liability of the user 210. The insurer 214 may utilizeboth local and third-party information to perform the analysis. Forexample, the insurer 214 may evaluate the infringement exposure value(s)in light of historical information associated with the user 210 and/orone or more additional users. For instance, the insurer 214 may look atother users having shared key characteristic data with the user 210(e.g., having a shared SIC code) to evaluate whether the infringementexposure value is too high or too low for a given industry.

Once the insurer 214 has analyzed the infringement exposure value(s),the insurer 214 may provide feedback data to the policy generationsystem 202. The policy generation system 202 may utilize the feedbackdata to perform any necessary adjustments to the infringement exposurevalue(s). For example, if the feedback data indicates that theinfringement exposure value is too low for a particular revenue source,the policy generation system 202 may increase the infringement exposurevalue for the revenue source. In addition, the insurer feedback mayinclude feedback regarding particular terms to be included in theinsurance policy. For example, in response to determining that theinfringement exposure value(s) associated with the user 210 should behigher, the insurer 214 may also provide feedback regarding theco-insurance value the user 210 will be responsible for (e.g.,indicating that a higher deductible value should be outlined in thepolicy).

In the third phase 208, a policy-term generation component of the policygeneration system 202, may determine and/or generate one or more termsof a policy quote and/or an insurance policy. For example, utilizing theinitial and/or adjusted infringement exposure value(s), along with theinsurer feedback, the policy-term generation component may determine oneor more terms and conditions of a potential insurance policy for theuser. Alternatively, or in addition, the policy-term generationcomponent may generate a full insurance policy, outlining all terms andconditions of the policy. The policy generation system 202 may thenprovide an indication of the terms and/or the insurance policy to atleast one of the user 210 and/or the insurer 214 for acceptance. Forexample, the policy generation system 202 may initially provide anindication of the terms and/or policy to the user 210. Upon acceptanceof the terms, the policy-term generation component may generate a fullinsurance policy and/or may provide an indication of the acceptance toone or more insurers to generate the policy. Alternatively, thepolicy-term generation component may generate a full insurance policyand provide the insurance policy to the user 210 and/or the insurer 214for acceptance. Once the policy is accepted by both the user 210 and theinsurer 214, the policy may be finalized by the insurer 214 and becomeeffective.

FIG. 3 illustrates an example server computing device that may be usedfor IP liability protection. As described herein, one or more usercomputing devices, such as the user device(s) 102 of FIG. 1 , cancommunicate with one or more intermediary computing devices, such as thepolicy generation system 300 described herein. The server computingdevice(s) 302 (“server(s)” hereinafter) can include one or more serversor other types of computing devices that can be embodied in any numberof ways. For example, in the example of a server, the modules, otherfunctional components, and data can be implemented on a single server, acluster of servers, a server farm or data center, a cloud-hostedcomputing service, a cloud-hosted storage service, and so forth,although other computer architectures can additionally or alternativelybe used.

Further, while the figures illustrate the components and data of theserver(s) 302 as being present in a single location, these componentsand data can alternatively be distributed across different computingdevices and different locations in any manner. In some examples, suchcomponents and data can be distributed across user computing devices, asdescribed herein. The functions can be implemented by one or more servercomputing devices, with the various functionality described abovedistributed in various ways across the different computing devices.Multiple server(s) 302 can be located together or separately, andorganized, for example, as virtual servers, server banks and/or serverfarms.

In some examples, the server(s) 302 may perform the same or similarfunctions as the policy generation system described in FIGS. 1 and 2 .The server(s) 302 may comprise processor(s) 304 that are operativelyconnected to network interface(s) 306 and a computer-readable media 308.Each processor 304 can be a single processing unit or a number ofprocessing units and can include single or multiple computing units ormultiple processing cores. The processor(s) 304 can be implemented asone or more microprocessors, microcomputers, microcontrollers, digitalsignal processors, central processing units, state machines, logiccircuitries, and/or any devices that manipulate signals based onoperational instructions. For example, the processor(s) 304 can be oneor more hardware processors and/or logic circuits of any suitable typespecifically programmed or configured to execute the algorithms andprocesses described herein. The processor(s) 304 can be configured tofetch and execute computer-readable instructions stored in thecomputer-readable media 308, which can program the processor(s) 304 toperform the functions described herein.

The computer-readable media 308 can include volatile and nonvolatilememory and/or removable and non-removable media implemented in any typeof technology for storage of information, such as computer-readableinstructions, data structures, program modules, or other data. Suchcomputer-readable media 308 can include, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, optical storage,solid state storage, magnetic tape, magnetic disk storage, RAID storagesystems, storage arrays, network attached storage, storage areanetworks, cloud storage, or any other medium that can be used to storethe desired information and that can be accessed by a computing device.Depending on the configuration of the server(s) 302, thecomputer-readable media 308 can be a type of computer-readable storagemedia and/or can be a tangible non-transitory media to the extent thatwhen mentioned, non-transitory computer-readable media exclude mediasuch as energy, carrier signals, electromagnetic waves, and signals perse.

The computer-readable media 308 can be used to store any number offunctional components that are executable by the processor(s) 304. Inmany implementations, these functional components comprise instructionsor programs that are executable by the processor(s) 304 and that, whenexecuted, specifically configure the one or more processors 304 toperform the actions attributed above to the automated negotiationsystem. Functional components stored in the computer-readable media 308can include a user data component 310, characteristic data component312, infringement exposure value(s) component 314, feedback datacomponent 316, threshold data component 318, policy-term generationcomponent 322, as well as a notification component 324.

In examples, the computer-readable media 308 may include the user datacomponent 310. The user data component 310 may be configured to receive,access, and/or store user-provided data 326, historical user data 328,and/or third-party data 330. The user data component 310 may beconfigured to receive, access, and/or store data associated with a userand/or the user's business. For example, the buyer data component 310may be configured to receive user-provided data 326 from a user alongwith a request for an insurance policy quote and/or in response to arequest for information. The user-provided data 326 may include dataassociated with one or more key characteristics of the user. Forexample, the user-provided data 326 may include information associatedwith an industry classification of each sector of the user's business(e.g., an SIC code for each individual unit of the business), employmentinformation (e.g., a number of employees overall, a number of employeeswithin each business unit, etc.), revenue information, a litigationhistory, whether the company is publicly traded or privately held,and/or IP information related to each of the user's IP assets.

The user data component 310 may further include historical user data328. For example, the user data component 310 may be configured toreceive, access, and/or store user-historical user data 328 associatedwith the user. The historical user data 328 may include data associatedwith historical transactions between the user and the policy generationsystem and/or user preferences, such as references regarding userinterface settings, notification preferences, and the like, for example.Additionally, the user data component 310 may further includethird-party data 330. For example, the user data component 310 mayinclude data received and/or accessed from a third-party database. Thethird-party data 330 may be associated with the key characteristicsassociated with the user. For example, the system may determine that theuser-provided data 326 and/or the historical user data 328 does notinclude all of the necessary information associated with the keycharacteristics. In response, the system may submit a request to one ormore third-party databases for additional information. Alternatively, orin addition, the system may be configured to receive informationassociated with the user from the one or more third-party databases. Theuser data component 310 may be configured to access and/or receive suchdata, as described herein, via the network interface 306 of the servercomputing device 302.

In examples, the computer-readable media 308 may further include acharacteristic data component 312. The characteristic data component 312may be configured to generate characteristic data associated with thekey characteristics of the user described herein. For example, thecharacteristic data component 312 may be configured to access receiveand/or access data from the user data component 310. The characteristicdata component 312 may analyze the data to generate characteristic datarepresenting each of the key characteristics of the user. For example,the characteristic data component 312 may be configured to access theuser-provided data 326 of the user data component 310. The user-provideddata 326 may include employment information associated with the user.The characteristic data component 310 may then analyze the employmentinformation to generate characteristic data indicating an employee count(e.g., a key characteristic) for each unit of the user's business. Inanother example, the user-provided data 326 may include revenueinformation associated with the user, such as financial statements,fiscal reports, tax documents, and the like. The characteristic datacomponent 310 may then analyze the revenue information to generatecharacteristic data indicating an annual revenue (e.g., a keycharacteristic) for each unit of the user's business. In a furtherexample, the characteristic data component 312 may be configured toaccess the third-party data 330 of the user data component 310. Thethird-party data 330 may include information associated with alitigation history of the user. The characteristic data component 310may analyze the litigation history information to determine keycharacteristics of the user, such as litigation costs incurred on anannual basis, settlement terms, and the like, for example.

The computer-readable media 308 may further include an infringementexposure value(s) component 314. The infringement exposure value(s)component 314 may be configured to determine one or more infringementexposure values associated with the user, and/or each revenue source ofthe user, described herein. The infringement exposure value(s) mayinclude one or more values indicating a predicted exposure that a legalclaim may be brought against the user. As described herein, a legalclaim or infringement claim may include any claim, brought against theuser by a third-party, that the user has, by the exercise of its rightsin association with an revenue source, infringed on or misappropriatedthe IP of a third party, including patent infringement resulting fromthe manufacture, sale, or use of the borrower's product(s) orservice(s).

For example, the infringement exposure value(s) component 314 may beconfigured to receive and/or access characteristic data of thecharacteristic data component 312. The characteristic data may includedata representing each of the key characteristics associated with theuser. The infringement exposure value(s) component 314 may analyze thecharacteristic data to determine one or more infringement exposurevalue(s) associated with the user. For example, the infringementexposure value(s) component 314 may analyze the characteristic datarepresenting the key characteristics including the SIC code(s), employeecount, revenue size, litigation history, and/or publicly traded orprivately held nature of the user's business to predict an exposurevalue that the user will have a claim for infringement brought againstthem.

In addition, as described herein, the infringement exposure value(s)component 314 may utilize one or more machine learning techniques todetermine the infringement exposure value(s) associated with the user.For example, the infringement exposure value(s) component 314 mayexecute one or more algorithms (e.g., decision trees, artificial neuralnetworks, association rule learning, or any other machine learningalgorithm) to train the system to determine the one or more infringementexposure values. The training data may include characteristic data ofthe user, historical characteristic data, historical user data,transactional data, historical policy performance, and the like. Inexamples, the machine learning component(s) may execute any type ofsupervised learning algorithms (e.g., nearest neighbor, Naïve Bayes,Neural Networks, unsupervised learning algorithms, semi-supervisedlearning algorithms, reinforcement learning algorithms, and so forth).

In addition, in some examples, infringement exposure value(s) component314 may further analyze the characteristic data in light of the IPinformation associated with the user (e.g., the IP footprint). Forexample, when determining an exposure value for an revenue source of theuser, the infringement exposure value(s) component 314 may analyze thecharacteristic data in light of the IP footprint associated with therevenue source. In this example, if the user has IP protection (e.g.,issued patents, trademarks, etc.) in place for the revenue source, theinfringement exposure value determined may indicate a low exposure of aninfringement claim being brought against the user in association for theparticular revenue source (e.g., if the user has numerous issued patentsfor the revenue source, there may be a lower exposure that a claim willbe brought against the user, the cost of defending the claim may be low,the user is likely to prevail, etc.). In contrast, if the characteristicdata indicates that the user has an extensive litigation history forinfringement claims, the infringement exposure value(s) may indicatethat there is a high exposure that an infringement claim may be broughtagainst the user and/or that the cost of defending such claims may behigh, loss mitigation costs may be high, and the like, for example.

The computer-readable media 308 may further include feedback datacomponent 316. In examples, the feedback data component may beconfigured to store feedback data regarding the infringement exposurevalue(s) of the infringement exposure value(s) component 314. Forexample, the feedback data component 316 may be configured to receiveand/or access the infringement exposure value(s) and provide anindication of the infringement exposure value(s) to one or more insurersthat may carry the IP liability policy. In response to receiving anindication of the infringement exposure value(s), the insurer(s) mayprovide feedback regarding an accuracy of the value(s) (e.g., whetherthe value(s) are accurate, too high, too low, etc.). The feedback datacomponent 316 may be configured to receive the feedback data via thenetwork interface(s) 306 and store the feedback data for use by thesystem.

The computer-readable media 308 may further include a threshold datacomponent 318. In examples, the threshold data component 318 may includethreshold information associated with the infringement exposure value(s)indicating a predetermined threshold value under which the infringementexposure value(s) should fall and/or exceed. For example, the policygeneration system may be configured to analyze characteristic dataassociated with users to determine users that may be targeted for IPliability policies. For instance, users having a low exposure value maybe considered for targeted offerings or other insurance policyindications. In this example, the threshold data component 318 mayinclude information associated with a predetermined threshold valuewhich the infringement exposure value(s) should not exceed for the userto be targeted for a policy offering. Alternatively, in some examples,the predetermined threshold value may indicate values above which theinfringement exposure value(s) should fall for the user to be targetedfor a policy offering (e.g., in some examples the threshold value(s) mayindicate a minimum threshold above which the infringement exposure valuemust be).

In some examples, the threshold data may be provided by an insurer. Inother examples, the system may determine the threshold data value(s)based on one or more historical data points, such as historical dataassociated with one or more additional users (e.g., based on thresholdvalues associated with users that have not had an infringement claimbrought against them, users having low policy payout values, etc.). Forexample, the system may utilize one or more machine learning techniquesto determine the threshold data value(s). For example, a machinelearning component(s) (not shown) of the threshold data component 318may execute one or more algorithms (e.g., decision trees, artificialneural networks, association rule learning, or any other machinelearning algorithm) to train the system to determine the one or morethreshold values. In examples, the machine learning component(s) mayexecute any type of supervised learning algorithms (e.g., nearestneighbor, Naïve Bayes, Neural Networks, unsupervised learningalgorithms, semi-supervised learning algorithms, reinforcement learningalgorithms, and so forth).

In examples, the computer-readable media 308 may further include apolicy-term generation component 322. For example, the policy-termgeneration component 322 may be configured to receive and/or accessmetrics associated with insuring a user and/or the user response dataassociated with such metrics. The policy-term generation component 322may determine one or more terms of an IP liability policy and/orgenerate the IP liability policy for the user. For example, thepolicy-term generation component 322 may analyze the user response dataand determine that the user has accepted the metrics for insuring. Inresponse, the policy-term generation component 322 may generate an IPliability policy including at least the accepted metrics for insuringthe user.

Alternatively, or in addition, the policy-term generation component 322may be configured to receive and/or access data from the user datacomponent, the characteristic data component 312, infringement exposurevalue(s) component 314, and/or the feedback data component 316.Utilizing this data, the policy-term generation component 322 maydetermine one or more terms of a policy for the user. The policy-termgeneration component 322 may further determine other terms andconditions for insuring the user for inclusion in the policy. Forexample, the policy-term generation component 322 may further determinea co-insurance value (e.g., a deductible value the user is responsiblefor), indemnity provisions (e.g., which customer indemnities will becovered under the policy), loss mitigation terms (e.g., the terms underwhich loss mitigation techniques may be employed, coverage of costs forloss mitigation, etc.), and the like.

The policy-term generation component 322 may further be configured toprovide an indication of the one or more terms and/or the IP liabilitypolicy to the user and/or the insurer(s) for acceptance. Once theterm(s) and/or the policy have been accepted by the user and/or theinsurer(s), the policy-term generation component 322 may provide anindication of the user acceptance to the insurer(s). The insurer(s) mayutilize the indication of acceptance to finalize the policy.

The computer-readable media 308 may further include notificationcomponent 324. The notification component 324 may be configured toreceive and/or access and/or store indications from the user and/or theinsurer regarding notification preferences. For example, thenotification component 324 may receive and/or access the user-provideddata 326 of the user data component 310. The user-provided data 326 mayinclude information regarding preferences of the user for receivingnotifications from the policy generation system. For example, the usermay indicate preferences for a type of notification they wish to receive(e.g., an email, text, etc.), a preferred device for receivingindications, and the like. Further, the notification component 324 maybe configured to receive indications of user and/or insurer notificationpreferences directly from devices associated with the user and/orinsurer. For example, the notification component 324 may be configuredto receive indications of user preferences via the network interface(s)306.

FIGS. 4-6 illustrate various a flow diagrams of example processes for IPliability protection. The processes described herein are illustrated ascollections of blocks in logical flow diagrams, which represent asequence of operations, some or all of which may be implemented inhardware, software or a combination thereof. In the context of software,the blocks may represent computer-executable instructions stored on oneor more computer-readable media that, when executed by one or moreprocessors, program the processors to perform the recited operations.Generally, computer-executable instructions include routines, programs,objects, components, data structures and the like that performparticular functions or implement particular data types. The order inwhich the blocks are described should not be construed as a limitation,unless specifically noted. Any number of the described blocks may becombined in any order and/or in parallel to implement the process, oralternative processes, and not all of the blocks need be executed. Fordiscussion purposes, the processes are described with reference to theenvironments, architectures and systems described in the examplesherein, such as, for example those described with respect to FIGS. 1-3 ,although the processes may be implemented in a wide variety of otherenvironments, architectures and systems.

FIG. 4 illustrates a flow diagram of an example process 400 for IPliability protection. The order in which the operations or steps aredescribed is not intended to be construed as a limitation, and anynumber of the described operations may be combined in any order and/orin parallel to implement process 400. The operations described withrespect to the process 400 are described as being performed by one ormore individuals and/or teams of individuals. However, it should beunderstood that some or all of these operations may be performed by someor all of components, devices, and/or systems described herein.

At block 402, the system may receive a request for an insurance quote toinsure a user against a claim of intellectual property infringement. Forexample, a user may submit a request to the policy generation system foran IP liability policy (e.g., an insurance quote). The IP liabilitypolicy may include an insurance policy protecting the user againstclaims for IP infringement.

At block 404, the system may receive information indicating acharacteristic of the user, including at least one of businessinformation, intellectual property (IP) information, or legal claiminformation. For example, the system may be configured to receiveinformation associated with one or more key characteristics of the user.The key characteristics may be associated with business information(e.g., SIC code(s) associated with each unit of the user's business,employee count of each unit, a revenue size of the business and/or eachunit, and/or whether the user's business is privately held or publiclytraded), IP information (e.g., an IP footprint associated with eachrevenue source of the user), and/or legal claim information (e.g., alitigation history of the user, costs incurred in defense of IPinfringement claims, whether the user was the plaintiff or defendant,and/or the terms of the verdict/settlement).

In some examples, the user may provide the information along with theinitial request. Alternatively, or in addition, the system may beconfigured to request the information from the user in response to theinitial request and/or in response to receiving information from theuser. For example, in response to receiving the information from theuser along with the request, the system may determine that additionalinformation is required. In this instance, the system may be configuredto request additional information from the user. Still further, in someexamples, the system may be configured to receive information from oneor more databases having information associated with the keycharacteristics of the user, such as a local database and/or athird-party database.

At block 406, the system may generate, based at least in part on theinformation, characteristic data associated with the characteristic ofthe user. In examples, the system may be configured to analyze theinformation to generate characteristic data associated with one or morekey characteristics of the user. For instance, the system may beconfigured to analyze the information received from the user, requestedfrom the user, and/or accessed from the local and/or third-partydatabases(s) to generate the characteristic data. For example, thesystem may receive business information, such as expense reports, taxdocuments, revenue documents, and the like. The system may be configuredto analyze the business information to generate characteristic dataindicating a revenue size (e.g., annual revenue, quarterly revenue,etc.) of each unit of the user's business. The characteristic dataindicating the revenue size may be stored in association with the useras a key characteristic. As described herein, the key characteristicsmay include, but not be limited to, SIC code(s), employee count, revenuesize, litigation history, and/or the whether the user's business ispublicly or privately held. In addition, in some examples, the keycharacteristics may further include IP information indicating an IPfootprint of each revenue source of the user.

At block 408, the system may determine an infringement exposure valueassociated with the user based at least in part on the characteristicdata. For example, the system may be configured to analyze thecharacteristic data to determine one or more infringement exposurevalue(s) associated with the user. The infringement exposure value(s)may represent a value indicating a predicted exposure that a claim forIP infringement will be brought against the user. For example, thesystem may be configured to analyze the characteristic data associatedwith the litigation history of the user. If, for example, thecharacteristic data associated with the litigation history of the userindicates that the user has had numerous IP infringement claims assertedagainst them, the infringement exposure value associated with the usermay indicate a high predicted exposure that a future claim will bebrought against the user. In some examples, the infringement exposurevalue(s) may also be determined on a per revenue source basis. Forexample, the system may be configured to analyze the characteristic dataassociated with the litigation history of the user to determine apredicted exposure that an infringement claim will be brought againstthe user in association with a particular revenue source. In thisinstance, the system may be configured to analyze characteristic dataassociated with each revenue source of the user, individually. As such,the system may determine an infringement exposure value in associationwith each revenue source.

At block 410, the system may receive feedback data associated with theinfringement exposure value. For example, the system may be configuredto provide an indication of the infringement exposure value(s) to one ormore potential insurer(s) that will carry the policy. As such, thesystem may provide an indication of the infringement exposure value(s)to a device of the insurer(s).

The system may then receive feedback data from the insurer associatedwith the infringement exposure value(s). In examples, the system may beconfigured to receive feedback data generated by the insurer(s)regarding the infringement exposure value(s) that have been provided. Inresponse to receiving the infringement exposure value(s), the insurer(s)may evaluate the value(s), along with the characteristic data and/oradditional information associated with the user, to determine whetherthe infringement exposure value(s) accurately reflect the IP liabilityexposure (e.g., exposure of an infringement claim) associated with theuser. Based on the analysis, the insurer(s) may generate feedbackindicating whether the infringement exposure value(s) should remain asdetermined by the system or should be adjusted based on the analysis.The insurer(s) may then provide the feedback data to the system forfurther analysis.

At block 412, the system may determine at least one term of an insurancepolicy associated with the user. In examples, the system may beconfigured to determine one or more terms of the insurance policy and/orgenerate the insurance policy. For example, the terms may furtherinclude loss mitigation procedures, indemnity coverage, litigationprocedures, and the like. In examples, the system may also provide anindication of the one or more terms and/or the generated insurancepolicy to at least one of the user and/or the insurer for acceptance.For example, the system may be configured to provide the generatedpolicy to the user and/or the insurer(s) for acceptance. Once the userhas accepted the policy, the system may be configured to provide anindication of the acceptance to the insurer(s). As such, the insurer(s)may take steps to finalize the insurance policy.

FIG. 5 illustrates a flow diagram of an example process 500 for IPliability protection. The order in which the operations or steps aredescribed is not intended to be construed as a limitation, and anynumber of the described operations may be combined in any order and/orin parallel to implement process 500. The operations described withrespect to the process 500 are described as being performed by one ormore individuals and/or teams of individuals. However, it should beunderstood that some or all of these operations may be performed by someor all of components, devices, and/or systems described herein.

At block 502, the system may determine, based at least on characteristicdata associated with a user, an infringement risk value associated withthe user, the characteristic data associated with at least one ofbusiness information, intellectual property information, or legal claiminformation. For example, as described herein, the system may receive arequest for an insurance quote from a user. For example, as describedherein, the user may submit a request to the policy generation systemfor an IP liability policy (e.g., an insurance quote). The IP liabilitypolicy may include an insurance policy protecting the user againstclaims for IP infringement. For example, the insurance policy mayinclude terms for reimbursement of costs accrued in defense of an IPinfringement claim brought against the user.

Along with the request, the user may include information (e.g., anintake document) associated with one or more key characteristics of theuser. As described herein, the key characteristics may include, but notbe limited to, SIC code(s), employee count, revenue size, litigationhistory, and/or the whether the user's business is publicly or privatelyheld. In addition, in some examples, the key characteristics may furtherinclude IP information indicating an IP footprint of each revenue sourceof the user. In addition, in response to receiving the request, thesystem may be configured to request information associated with the keycharacteristics. Alternatively, or in addition, the system may beconfigured to receive information from and/or access one or more localand/or third-party databases having information associated with the keycharacteristics of the user. As described herein, utilizing theinformation from the user (provided and/or requested) and/or theinformation from the one or more databases(s), the system may analyzethe information to generate characteristic data associated with the keycharacteristics of the user.

In examples, the system may analyze the characteristic data associatedwith the user to determine infringement exposure value(s) associatedwith the user. For example, the system may be configured to analyze thecharacteristic data, determined from the user-provided and/or databaseinformation, to determine one or more infringement exposure value(s)associated with the user. In examples, the system may determineinfringement exposure value(s) indicating a predicted exposure that aclaim for IP infringement will be brought against the user. Inparticular, the infringement exposure value(s) may represent one or morevalues associated with a predicted exposure of IP liability of the userfor future claims. For example, the system may analyze thecharacteristic data associated with IP information for revenue sourcesof the user. The system may determine that the user has one or moreissued patents in associated with an revenue source of the user. Assuch, the system may determine an infringement exposure value(s)indicating a low predicted exposure that a claim for IP infringementwill be brought against the user and/or a low chance of large costs thatmay be accrued in associated with a claim (e.g., if the user has anissued patent for the revenue source, there is a low likelihood that aparty would assert a claim for infringement and/or a low likelihood thatthe party would prevail). In some examples, the infringement exposurevalue(s) may be determined on a per revenue source basis, or,alternatively, may represent an overall value associated with the user.

At block 504, the system may determine at least one term of an insurancepolicy associated with the user. For example, the terms may furtherinclude loss mitigation procedures, indemnity coverage (e.g., coveragein association with indemnity provisions the user has entered into withthird-parties), litigation procedures, and the like.

FIG. 6 illustrates a flow diagram of an example process 600 for IPliability protection. The order in which the operations or steps aredescribed is not intended to be construed as a limitation, and anynumber of the described operations may be combined in any order and/orin parallel to implement process 600. The operations described withrespect to the process 600 are described as being performed by one ormore individuals and/or teams of individuals. However, it should beunderstood that some or all of these operations may be performed by someor all of components, devices, and/or systems described herein.

At block 602, the system may access information indicating acharacteristic of one or more users. For example, the system may beconfigured to receive and/or access information associated with one ormore key characteristics of one or more users. As described herein, thekey characteristics may include, but not limited to, one or more SICcodes associated with the users, employee counts, revenue sizes,litigation histories, whether companies associated with the users arepublicly or privately held, and/or IP information associated with IPassets of the user. For example, the system may be configured to accessone or more local or third-party databases(s) storing informationassociated with key characteristics of users that may be potentiallyinsured. For instance, the system may be configured to access a localdatabase storing information associated with one or more users (e.g.,user-provided information, transactional data, etc.). Alternatively, orin addition, the system may be configured to access and/or receiveinformation from one or more third-party databases storing informationassociated with the key characteristics of the user(s). In someexamples, if the system is unable to gather all the necessaryinformation in associated with each of the key characteristics, thesystem may still proceed with the process.

At block 604, the system may generate, based at least in part on theinformation, characteristic data associated with the characteristic. Forexample, the system may be configured to analyze the accessedinformation to generate characteristic data associated with one or morekey characteristics of the potential users. For instance, the system maybe configured to analyze the information received from and/or accessedfrom the local and/or third-party databases(s) to determine and generatethe characteristic data. For example, the system may access informationregarding the industries, services, and/or product offerings associatedwith the individual users from a third-party database. The system may beconfigured to analyze the information to generate characteristic dataindicating one or more SIC codes of each business and/or business unitof the users. The characteristic data indicating the SIC codes may bestored in association with each user as a key characteristic.

At block 606, the system may determine, based at least in part on thecharacteristic data, one or more infringement exposure value(s)associated with individual users. In examples, the system may beconfigured to analyze the characteristic data of individual users todetermine one or more infringement exposure value(s) associated witheach of the users. As described herein, the infringement exposurevalue(s) may represent a value indicating a predicted exposure that aclaim for IP infringement will be brought against the individual users.

For example, the system may be configured to analyze the characteristicdata associated with the SIC code(s) of a user. For instance, thecharacteristic data associated with the SIC code(s) of the user mayindicate that the industry, product offerings, and/or services providedby the user are associated with a higher exposure that an infringementclaim being brought against the user. As such, the system may determinean infringement exposure value associated with the user indicating ahigh predicted exposure that a future claim will be brought against theuser. The system may perform a similar analysis for individual users ofthe potential users. In some examples, the infringement exposurevalue(s) may also be determined on a per revenue source basis. As such,the system may determine an infringement exposure value in associationwith each revenue source.

At block 608, the system may determine whether an infringement exposurevalue(s) of a user is below an exposure value threshold. For example,the system may receive and/or access exposure threshold values underwhich infringement exposure values should fall. In examples, potentialinsurers (e.g., insurers that may carry the policy) may provideinformation regarding infringement exposure value thresholds under whichinfringement exposure values should fall in order for the insurer toagree to carry the IP liability policy. Alternatively, or in addition,the system may determine, based on historical data, policy transactions,and the like, a threshold value under which the infringement exposurevalue should fall in order for a user to be considered low exposure(e.g., the system may determine that users having an infringementexposure value under the threshold value are less likely to have a claimbrought against them). Thus, at block 608, the system may analyzeinfringement exposure value(s) associated with individual users todetermine if the value(s) fall under the threshold value.

Alternatively, in some examples, the system may determine whether aninfringement exposure value of a user is above the exposure thresholdvalue. In this instance, a user having a low exposure may be associatedwith a high exposure value (e.g., in this example, a high infringementexposure value may indicate a low exposure of the user to infringementclaims).

If the system determines that the infringement exposure value(s) of theuser is not below the infringement exposure value threshold, then atblock 610 the system may determine that the user should not be engaged.For example, should the system that the infringement exposure value(s)of the user are not below the threshold (e.g., exceed the threshold),the system may determine that the user is a high exposure user (e.g.,has a high predicted exposure that in infringement claim will be broughtagainst them) and should not be engaged further. As such, the systemwill not target the user for policy offerings.

If, however, the system determines that the infringement exposure valueof the user is below the infringement exposure value threshold, then thesystem may send an indication associated with the insurance policy tothe user, as shown at block 612. For example, if the system determinesthat the infringement exposure value(s) of the user are below thethreshold, the system may determine that the user is a low exposure user(e.g., has a low predicted exposure that an infringement claim will bebrought against them) and therefore should be targeted for policyofferings. As such, the system may determine, based on the infringementexposure value(s), one or more terms for insuring the user.

At block 614, the system may receive input data associated withadditional information associated with the characteristic data. Forexample, if the user provides an indication that they have accepted theterms, the system may request additional information, as needed, tofinalize the policy. For example, the system may determine thatadditional information is needed in association with the keycharacteristics. As such, in response to sending the indication of thepreliminary terms and/or receiving the acceptance of the preliminaryterms, the system may provide the user with an intake documentrequesting the additional information. Alternatively, or in addition, inresponse to sending the indication of the preliminary terms and/orreceiving the acceptance of the preliminary terms, the system may causea GUI to be displayed having one or more input elements configured toreceive the input data associated with the additional information.

At block 616, the system may analyze the additional information todetermine a final term for insuring the user. In examples, the systemmay analyze the additional information to determine completecharacteristic data associated with the user. Based on thecharacteristic data, the system may determine an updated infringementexposure value(s) associated with the user and one or more finalizedterms for insuring the user. In addition, in some examples, the systemmay provide the updated information to the potential insurer(s) foranalysis. The insurer(s) may provide feedback data which, in turn, thesystem may utilize to determine final terms for insuring the user. Forexample, the insurer(s) may further analyze the updated information todetermine whether updated infringement exposure value(s) accuratelyreflect the IP liability of the user.

It should be understood that the example information described herein isused for illustrative purposes and is not by way of limitation. Theinformation sought and/or received from the discovery document may beany information associated with the borrower 110 and/or the intellectualproperty.

While the foregoing invention is described with respect to the specificexamples, it is to be understood that the scope of the invention is notlimited to these specific examples. Since other modifications andchanges varied to fit particular operating requirements and environmentswill be apparent to those skilled in the art, the invention is notconsidered limited to the example chosen for purposes of disclosure andcovers all changes and modifications which do not constitute departuresfrom the true spirit and scope of this invention.

Although the application describes embodiments having specificstructural features and/or methodological acts, it is to be understoodthat the claims are not necessarily limited to the specific features oracts described. Rather, the specific features and acts are merelyillustrative some embodiments that fall within the scope of the claims.

1. (canceled)
 2. A system, comprising: one or more processors; andnon-transitory computer-readable media storing instructions that, whenexecuted by the one or more processors, cause the one or more processorsto perform operations comprising: receiving a request for an insurancequote to insure a entity against a claim of intellectual propertyinfringement; receiving information indicating a characteristic of theentity, the information including at least one of: business information;intellectual property information; or legal claim information;generating, based at least in part on the information, characteristicdata associated with the characteristic of the entity; training amachine learning model configured to determine infringement exposurevalues associated with entities utilizing a training dataset includinghistorical policy performance metrics such that a trained machinelearning model is generated to weight characteristics of the entities;determining, based at least in part on the characteristic data andutilizing the trained machine learning model, an infringement exposurevalue associated with the entity; receiving feedback data associatedwith the infringement exposure value; determining, based at least inpart on the infringement exposure value and the feedback data, a costfor insuring the entity against the claim of intellectual propertyinfringement; and determining at least one term of an insurance policyassociated with the entity, the insurance policy including at least thecost.
 3. The system of claim 2, wherein determining the infringementexposure value comprises determining the infringement exposure valuebased at least in part on a revenue source associated with the entity.4. The system of claim 2, wherein: the business information includes atleast an industry classification, a number of employees, or a revenuevalue; the intellectual property information includes at least one of: anumber of intellectual property assets associated with the entity; atype of intellectual property of individual ones of the intellectualproperty assets; a licensing agreement associated with at least one ofthe intellectual property assets; or a second insurance policyassociated with the at least one of the intellectual property assets;and the legal claim information includes at least one of: a litigationhistory associated with the entity; information associated withinfringement allegations asserted against the entity; or resolutioninformation including at least one of settlement terms, damages, orinformation indicating whether the entity prevailed.
 5. The system ofclaim 2, wherein the at least one term of the insurance policy includesat least one of: a reimbursement provision covering a cost associatedwith defending against the claim of intellectual property infringement;a settlement cost; a damages cost; legal costs; or an indemnity cost. 6.The system of claim 2, the operations further comprising: determining afirst liability cost associated with the entity, wherein the firstliability cost represents a first cost which the entity is responsiblefor in defense of an infringement allegation; determining a secondliability cost associated with an insurer, wherein the second liabilitycost represents a second cost which the insured is responsible for inthe defense of the infringement allegation; and wherein the at least oneterm includes the first liability cost and the second liability cost. 7.The system of claim 2, wherein receiving the information indicating thecharacteristic of the entity comprises: causing display of a graphicalentity interface to the entity, the graphical entity interface includingan element configured to receive input data associated with theinformation; and receiving the input data, the input data including atleast the information.
 8. The system of claim 2, wherein: receiving theinformation indicating the characteristic of the entity comprises:accessing one or more databases having first data associated with theentity; identifying a given characteristic for which second data isunavailable in the one or more databases; sending, based at least inpart on identifying the given characteristic, an intake documentincluding an element requesting the second data; and receiving thesecond data; and determining the characteristic data comprisesdetermining the characteristic data based at least in part on the seconddata.
 9. A method comprising: training a machine learning modelconfigured to determine infringement exposure values associated withentities utilizing a training dataset including historical policyperformance metrics such that a trained machine learning model isgenerated to weight characteristics of the entities; determining, basedat least in part on characteristic data associated with an entity andutilizing the trained machine learning model, an infringement exposurevalue associated with the entity, the characteristic data associatedwith at least one of: business information; intellectual propertyinformation; or legal claim information; determining, based at least inpart on the infringement exposure value, a cost for insuring the entityagainst a claim of intellectual property infringement; and determiningat least one term of an insurance policy associated with the entity, theinsurance policy including at least the cost.
 10. The method of claim 9,wherein determining the infringement exposure value comprises: causingdisplay of a graphical entity interface to the entity, the graphicalentity interface including an element configured to receive input dataassociated with at least one of the business information, theintellectual property information, or the legal claim information; andreceiving the input data.
 11. The method of claim 10, wherein: thebusiness information includes at least an industry classification, anumber of employees, or a revenue value; the intellectual propertyinformation includes at least one of: a number of intellectual propertyassets associated with the entity; a type of intellectual property ofindividual ones of the intellectual property assets; a licensingagreement associated with at least one of the intellectual propertyassets; or a second insurance policy associated with the at least one ofthe intellectual property assets; and the legal claim informationincludes at least one of: a litigation history associated with theentity; information associated with infringement allegations assertedagainst the entity; or resolution information, the resolutioninformation including at least one of settlement terms, damages, orwhether the entity prevailed.
 12. The method of claim 9, furthercomprising: receiving, from at least one of a device associated with aninsurer or at least one insurer, feedback data associated with theinfringement exposure value; and wherein determining the cost comprisesdetermining the cost based at least in part on the feedback data. 13.The method of claim 12, further comprising: receiving a first indicationof acceptance of the insurance policy by the entity; receiving a secondindication of acceptance of the insurance policy by the insurer; and inresponse to receiving the first indication and the second indication,issuing the insurance policy.
 14. The method of claim 12, furthercomprising: determining a first liability cost associated with theentity, wherein the first liability cost represents a first cost whichthe entity is responsible for in defense of an infringement allegation;determining an second liability cost associated with the insurer,wherein the second liability cost represents a second cost which theinsurer is responsible for in the defense of the infringementallegation; and wherein the insurance policy further includes the firstliability cost and the second liability cost.
 15. The method of claim 9,wherein the at least one term of the insurance policy includes at leastone of: a reimbursement provision covering a cost associated withdefending against the claim of intellectual property infringement; asettlement cost; a damages cost; legal cost; or an indemnity cost.
 16. Asystem comprising: one or more processors; and non-transitorycomputer-readable media storing instructions that, when executed by theone or more processors, cause the one or more processors to performoperations comprising: training a machine learning model configured todetermine infringement exposure values associated with entitiesutilizing a training dataset including historical policy performancemetrics such that a trained machine learning model is generated toweight characteristics of the entities; analyzing characteristic dataassociated with one or more of the entities to determine one or moreexposure values associated with individual entities of the one or moreentities, the exposure value associated with a claim of intellectualproperty infringement; identifying, based at least in part on the one ormore exposure values, an entity of the one or more entities having anexposure value below an exposure value threshold; determining, based atleast in part on the exposure value, a preliminary cost for insuring theentity; determining an insurance policy indication associated with theentity, the insurance policy indication including at least thepreliminary cost for insuring the entity; and sending the insurancepolicy indication to the entity.
 17. The system of claim 16, theoperations further comprising: receiving first information associatedwith a characteristic of the one or more entities, the data including atleast one of: business information; intellectual property information;or legal claim information; and generating, based at least in part onthe information, characteristic data associated with the characteristicof the one or more entities.
 18. The system of claim 17, wherein theinsurance policy indication requests second information associated withthe characteristic data.
 19. The system of claim 18, the operationsfurther comprising: receiving the input data associated with the secondinformation; analyzing the second information to determine a final costfor insuring the entity; and determining at least one term of aninsurance policy associated with the entity, the term of the insurancepolicy including at least the final cost.
 20. The system of claim 19,wherein analyzing the second information to determine the final cost forinsuring the entity comprises: analyzing the second information todetermine additional characteristic data associated with thecharacteristic; analyzing the characteristic data and the additionalcharacteristic data to determine an additional exposure value associatedwith the entity; and determining, based at least in part on theadditional exposure value, a final cost for insuring the entity.
 21. Thesystem of claim 20, the operations further comprising: receiving, fromat least one of a device associated with an insurer or the insurer,feedback data associated with the additional exposure value; and whereindetermining the final cost for insuring the entity is further based atleast in part on the feedback data.